Execute a constrained read-only SQL rowset query over authorized datasets. Use this only for wide reports that do not fit the governed exact-query surface. SQL must be a single SELECT/WITH statement over the provided dataset aliases.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | Single read-only SELECT or WITH statement. | |
| sources | Yes | Authorized datasets made available to the SQL report. | |
| max_rows | No | Optional row cap, up to the API maximum. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses read-only constraint and requirement for authorized datasets, but does not detail behavior on invalid SQL, pagination, or row limits beyond the schema. Missing elements like error handling or performance implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise, front-loaded sentences with no redundancies. Every sentence adds value: purpose, usage guidance, and SQL constraint.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given lack of output schema and moderate complexity (3 params, no nested objects), the description covers key aspects: read-only, authorized datasets, SQL constraint, and usage guidance. Slight gap: does not mention max_rows behavior or output format, but schema covers max_rows.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so description adds minimal value beyond the schema. The description restates that SQL must be SELECT/WITH (already implied by schema) but does not provide additional parameter-level insights beyond what is in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'execute' and resource 'constrained read-only SQL rowset query over authorized datasets'. It distinguishes from the 'governed exact-query surface', implying a different tool (e.g., query_data) for exact queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this only for wide reports that do not fit the governed exact-query surface', providing a clear when-to-use and when-not-to-use directive. Also states SQL must be a single SELECT/WITH statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.
Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.
With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.
The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.
